TY  - JOUR
VL  - 1116
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187788290&doi=10.1007%2f978-981-99-8646-0_10&partnerID=40&md5=26ccda5ca498b76300e36eb5db4b01b2
A1  - Lakhina, U.
A1  - Elamvazuthi, I.
A1  - Badruddin, N.
A1  - Jangra, A.
A1  - Huy, T.H.B.
A1  - Guerrero, J.M.
JF  - Lecture Notes in Electrical Engineering
Y1  - 2024///
KW  - Energy management; Heuristic algorithms; Microgrids; Optimization
KW  -  And renewable energy resource; Cost minimization; Costs Optimization; Generation cost; Local optima; Meta-heuristics algorithms; Microgrid; Near-optimal; Optimization algorithms; Optimization method
KW  -  Renewable energy
ID  - scholars20129
N2  - Optimization methods are applied to discover a near optimal or optimal solution for any distinguished problem. Many researchers have applied different optimization techniques on microgrids for cost optimization. In this paper, an improved multi-verse optimizer algorithm is proposed for generation cost minimization in microgrids. Two modifications are done in the original algorithm for solving local optima problem and improving exploration and exploitation process. Local optima problem is solved using average positioning and universe position updating equation is improved by hybridizing it with sineâ??cosine algorithm. Thus, the simulation results reported by the investigated algorithms show that the proposed algorithm outperforms the other algorithms in minimizing generation cost and reducing computation time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
EP  - 124
SN  - 18761100
PB  - Springer Science and Business Media Deutschland GmbH
SP  - 111
TI  - Generation Cost Minimization in Microgrids Using Optimization Algorithms
N1  - cited By 0; Conference of 3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023 ; Conference Date: 15 May 2023 Through 17 May 2023; Conference Code:308669
AV  - none
ER  -